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7 tips for effective system prompting: A developer's guide to building better AI applications

As AI becomes increasingly central to modern software development, the ability to craft effective system prompts has emerged as a crucial skill. Whether you’re building a code generation tool, creating a chatbot, or developing AI-powered features, your success largely depends on how well you can communicate with AI models through prompts. At CircleCI, we’ve spent countless hours working with developers who are integrating AI into their applications.

AI Cost Optimization Strategies For AI-First Organizations

Not long ago, our co-founder and CTO, Erik Peterson, shared some insights on AI spending. He shared how AI costs currently fall under the write-off-friendly world of R&D. He also acknowledged why DevOps teams might feel it’s too early to start optimizing AI costs. As the saying goes, “Premature optimization is the root of all evil.” But after more than a decade of software development, Erik knows that eventually, research, experimentation, and big ideas need to deliver real returns.

Unlocking Ultimate PC Performance: The Art of Bottleneck Busting

Welcome, Tech Explorer, to the grand journey of maximizing your PC's potential. Whether you're an AI wizard optimizing high-performance computing or a casual gamer frustrated by unexpected stutters, one enemy stands between you and peak efficiency: the hardware bottleneck. That's where the pc bottleneck calculator steps in-your secret weapon in the battle against system slowdowns.

Accelerating AI with open source machine learning infrastructure

The landscape of artificial intelligence is rapidly evolving, demanding robust and scalable infrastructure. To meet these challenges, we’ve developed a comprehensive reference architecture (RA) that leverages the power of open-source tools and cutting-edge hardware.

Unlocking Edge AI: a collaborative reference architecture with NVIDIA

The world of edge AI is rapidly transforming how devices and data centers work together. Imagine healthcare tools powered by AI, or self-driving vehicles making real-time decisions. These advancements rely on bringing AI directly to edge devices. However, building a robust architecture for diverse edge environments presents significant hurdles. This blog introduces our new reference architecture, designed to simplify edge AI deployment.

Building optimized LLM chatbots with Canonical and NVIDIA

The landscape of generative AI is rapidly evolving, and building robust, scalable large language model (LLM) applications is becoming a critical need for many organizations. Canonical, in collaboration with NVIDIA, is excited to introduce a reference architecture designed to streamline and optimize the creation of powerful LLM chatbots. This solution leverages the latest NVIDIA AI technology, offering a production-ready AI pipeline built on Kubernetes.